Teaching
Fall 2026–2027Course materials, lecture slides (PDF), and live interactive Google Colab notebooks across active courses.
ECO447 İktisatçılar İçin Makine Öğrenmesi (Machine Learning for Economists)
Undergraduate • Department of Economics, Hacettepe UniversityBridge between econometrics and ML: supervised models, regularization, trees, causality, and clustering.
| Week | Topic | Empirical Lab / Dataset | Materials |
|---|---|---|---|
| W1 | Introduction to Machine Learning | California Housing & Macro ETFs | Slides Python Fundamentals |
| W2 | Causality: Theory & Data | Causality & Empirical Identification | Slides |
| W3 | Data Analysis & Exploratory Data Analysis (EDA) | Exploratory Data Analysis Workflow | Slides |
| W4 | Introduction to Regression | Linear Regression Modeling | Slides |
| W5 | Logistic Regression & Classification | Classification Metrics & Logit | Slides |
| W6 | Regularization: Ridge & LASSO | Penalized Regression & CV | Slides |
| W7 | Decision Trees | Tree-Based Modeling & Pruning | Slides |
| W8 | Advanced DT: Classification & Regression | Advanced Tree Architectures | Slides |
| W9 | Model Trees | Model Trees on Economic Data | Slides |
| W10 | Ensemble Methods | Random Forests & OOB Estimation | Slides |
| W11 | Boosting: AdaBoost | AdaBoost Implementation & Tuning | Slides |
| W12 | Causal Trees | Heterogeneous Treatment Effects | Slides |
| W13 | Clustering | K-Means & Hierarchical Clustering | Slides |
| W14 | Similarity Measures & Term Review | Metric Space & Distances in Python | Slides |
SEN608 Veri Analitiği ve İstatistik (Data Analytics and Statistics)
M.Sc. (Thesis) in Systems Engineering • Hacettepe University
Assessment: Weekly HW (26%), Midterm (25%), Research Presentations (15%), Final Exam (34%).
Reference Documents:
Final Project Guide
Midterm Paper
Midterm Solutions
| Week | Topic & Syllabus Mapping | Applied Focus | Materials |
|---|---|---|---|
| W1 | Statistics, Data Science and Python Setup | Colab & Pandas Fundamentals | Slides Python Fundamentals |
| W2 | Data Exploration & EDA | Distributions, Boxplots, Outliers | Slides |
| W3 | Inference Basics: Permutation & p-values | Randomization Tests in Python | Slides |
| W4 | Random Numbers & Simulation | Monte Carlo Simulation | Slides |
| W5 | Probability & the Normal Distribution | Theoretical Distributions & QQ-Plots | Slides |
| W6 | Categorical Data & Chi-Square Tests | Contingency Tables & Goodness-of-Fit | Slides |
| W7 | Midterm Review & Examination | Midterm Assessment | Review |
| W8 | Sampling & Bootstrap Confidence Intervals | Resampling & Empirical CIs | Slides |
| W9 | ANOVA Basics | One-way & Two-way ANOVA | Slides |
| W10 | Correlation & Association | Pearson, Spearman, and Collinearity | Slides |
| W11 | Simple Linear Regression | OLS Diagnostics with Statsmodels | Slides |
| W12 | Multiple Regression | Multivariate Model Diagnostics | Slides |
| W13-14 | Research Presentations | Student Term Presentations | Slides |
BSM622 Veri Analitiği ve İstatistik
Tezsiz Yüksek Lisans Programı • Hacettepe Üniversitesi Bilişim Enstitüsü
Değerlendirme: Haftalık Ödevler (%26), Ara Sınav (%25), Proje Sunumları (%15), Final Sınavı (%34).
Ders Kapsamı: Bilişim ve mühendislik alanına yönelik veri ön işleme, çıkarımsal istatistik, permütasyon testleri, ANOVA ve regresyon modellemesi.
| Hafta | Haftalık Konu ve Teorik Çerçeve | Uygulama / Laboratuvar | Materyaller |
|---|---|---|---|
| H1 | İstatistik, Veri Bilimi ve Python Kurulumu | Colab & Pandas Temelleri | Slayt Python Temelleri |
| H2 | Veri Keşfi ve Keşifçi Veri Analizi (EDA) | Dağılımlar, Kutu Grafikleri, Aykırı Değerler | Slayt |
| H3 | Çıkarımsal İstatistik: Permütasyon ve p-değerleri | Rassallaştırma Testleri | Slayt |
| H4 | Rassal Sayılar ve Simülasyon | Monte Carlo Simülasyonu | Slayt |
| H5 | Olasılık ve Normal Dağılım | Teorik Dağılımlar & QQ Çizimleri | Slayt |
| H6 | Kategorik Veriler ve Ki-Kare Testleri | Çapraz Tablolar & Uyum İyiliği | Slayt |
| H7 | Dönem İçi Tekrarı ve Ara Sınav | Ara Sınav Uygulaması | Tekrar |
| H8 | Örnekleme ve Bootstrap Güven Aralıkları | Yeniden Örnekleme & Ampirik Aralıklar | Slayt |
| H9 | Varyans Analizi (ANOVA) Temelleri | Tek ve İki Yönlü ANOVA | Slayt |
| H10 | Korelasyon ve İlişki Ölçüleri | Pearson, Spearman ve Çoklu Doğrusallık | Slayt |
| H11 | Basit Doğrusal Regresyon | Statsmodels ile EKK Teşhisleri | Slayt |
| H12 | Çoklu Doğrusal Regresyon | Çok Değişkenli Model Teşhisleri | Slayt |
| H13-14 | Dönem Araştırma Projesi Sunumları | Öğrenci Proje Sunumları | Slayt |
SEN605 Finansal Sistemler ve Karar Verme (Financial Systems and Decision Making)
Graduate School of Informatics • Hacettepe University
Workload & Assessment: Presentation (20%), Midterm (30%), Final Exam (50%).
Course Focus: Systems engineering principles applied to financial markets, volatility modeling (GARCH), recurrent architectures, network contagion, and financial sentiment analysis.
| Week | Topic & Theoretical Scope | Notebook / Lab Focus | Materials |
|---|---|---|---|
| W1 | Intro to Systems Engineering & Financial Systems | Setup: yfinance, pandas, stock data | Slides Python Fundamentals |
| W2 | Financial Data Structures & Python Ecosystem | BIST-100 & S&P 500 OHLCV pipeline | Slides |
| W3 | Financial Time Series: Stationarity & ARIMA | USD/TRY ARIMA modeling | Slides |
| W4 | Volatility in Financial Systems: ARCH & GARCH | GARCH on BIST-30 equities | Slides |
| W5 | ML in Financial Forecasting I: Linear & Logistic | Predicting BIST-100 market direction | Slides |
| W6 | ML in Financial Forecasting II: Trees & Ensembles | XGBoost return predictor with SHAP | Slides |
| W7 | Deep Learning: Foundations of ANNs | PyTorch MLP for volatility prediction | Slides |
| W8 | MIDTERM EXAM | Theory & Applied Examination | Exam Session |
| W9 | Dynamic System Models: RNN, LSTM & GRU | LSTM 5-day forecast for USD/TRY & BTC | Slides |
| W10 | Financial Text Mining & NLP | TF-IDF + LDA on BIST earnings releases | Slides |
| W11 | Financial Sentiment Analysis with LLMs (FinBERT) | FinBERT pipeline for news headlines | Slides |
| W12 | Network Theory & Financial Contagion Effect | BIST-100 MST network visualization | Slides |
| W13 | Financial Risk Analysis & Portfolio Optimization | Monte Carlo VaR & Efficient Frontier | Slides |
| W14 | General Review & Project Clinic | Feedback on term projects & code review | Slides |